The above-referenced patents to Cooper et al. disclose methods and apparatus (systems) that can separate and classify "patterns" or real-world "events" that are not linearly separable. The patterns or events in the outside world can be detected and described by a set of measurements, the results of which are represented by an input signal S comprised of individual signals s.sub.1, s.sub.2 . . . s.sub.k. The signal S could be, for example, a signal coming from a camera registering a scene (pattern) or the output of a microphone detecting some arbitrary sound (pattern).
In a system comprising a Nestor.TM. adaptive module as described in these patents, all input signals S (which are themselves referred to herein as "patterns") belonging to the same class should elicit the same final response from the system. For example, in the context of an application such as character recognition, any version of a handdrawn "2" seen by the system should result in an output signal which causes the character font member for "2" to be displayed on some output device, such as a video screen, printout, etc.
A system of this type, or so-called Nestor System.TM., is an extremely powerful pattern class separator and identifier. For example, this system may be trained with a learning procedure in which the operator need have no knowledge of the complex geography of the multi-dimensional space in which the pattern class separation and identification is being performed. Such a system requires the input signal S to be preprocessed into an intermediate signal F which represents only certain prescribed features of the original patterns. The input signal S normally contains too much irrelevant information for effective pattern recognition by the Nestor System.
Subjecting the input signal S (representing the pattern) to this preprocessing step--referred to herein as "encoding"--should preserve enough information to permit patterns to be distinguished from each other. Information that is irrelevant for learning one class may be important to distinguish some other. For this reason, it may be difficult to choose a single preprocessing strategy that removes all irrelevant information without jeopardizing the ability to distinguish some classes.
The system we describe here can be thought of, in one sense, as a way of linking together a number of such Nestor Systems. Each component Nestor System can be regarded as a complete unit, including its own preprocessing and encoding procedures. A pattern is identified by the responses it produces among these component units. Each unit has its own encoding procedures, different from that of any other. Therefore, it is sensitive to certain types of information in the input signal. The particular set of features it registers may give it a special aptitude for learning some types of pattern classes, but not others. A class will be learned automatically by that unit with the best aptitude for learning it. At the same time, learning other pattern classes may require pooling the resources of several component units, none of which alone has sufficient discriminating skills, by virtue of its preprocessing and encoding properties, to distinguish these classes. In these cases, the system identifies an example of this class by correlating the responses of a set of units.
As an example of this, consider once again the task of identifying hand-drawn characters. The pattern properties registered by one unit may give emphasis to certain aspects of the characters but not to others. Specifically, the extracted features may serve well to distinguish among characters with rectilinear shapes, such as "A", "H", "K", "N", etc. but may not provide good separation between characters with circular shapes, such as "B", "C", "Q", etc. Consequently, this unit will distinguish rectilinear shapes while a unit that encodes circular features could learn to separate pattern classes in which circular features are present and distinctive.
Encoding (feature extraction) schemes for hand-drawn characters, audible sounds and numerous other types of patterns are well known in the art and are described in the literature. An overview of this technology for hand-drawn characters is given in Automatic Recognition of Handprinted Characters--the State of the Art, by C. Y. Suen, M. Berthod and S. Mori, Proceedings IEEE, Vol. 68, No. 4, April 1980, pp. 469-487.
Not every unit of a Nestor System learns every class. Further, each unit is only looking at a fraction of all the possible pattern properties registered by the system as a whole. Consequently, learning of a pattern class occurs more quickly and more economically (in terms of the size of system memory) than if the system consisted of a single unit which combined the preprocessing and coding of all the individual units in the system. Another advantageous and novel feature of this system and its architecture is that it allows the possibility of incorporating new units into the system over the course of training. This is an especially useful property if the kind of pattern classes that the system is expected to deal with changes substantially over time. Additional units may be incorporated in the course of the system's experience without, in general, the need to retrain the system on the pattern classes it has already learned.
Advantageously, each Nestor adaptive module can be trained to be exceedingly adept and accurate in its classification of certain types of patterns. This sophistication permits a module to classify based on extremely subtle differences between patterns provided that the unit has the necessary preprocessing and encoding to sense those diiferences. A high level of ability to classify patterns based on one set of criteria does not preclude the ability to use another set of criteria (from another module or set of modules) to classify based on a different set.
The Nestor adaptive module is, itself, a well known device for pattern classification and identification. This device is described, inter alia, in the aforementioned U.S. Pat. Nos. 3,950,733; 4,044,243; 4,254,474 and 4,326,259, to Cooper et al.